National and Subnational estimates for the United States of America

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United States of America. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods for further explanation).

Table of Contents


Using data available up to the: 2020-04-08

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-03-29) in the United States of America, stratified by state, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-03-29)

Table 1: Latest estimates (as of the 2020-03-29) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time, and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 30285 (29683 – 30906)
Expected change in daily cases Increasing
Effective reproduction no. 1.2 (1.1 – 1.3)
Doubling time (days) 24 (20 – 30)
Adjusted R-squared 0.92 (0.87 – 0.97)

Reported confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates are shown until the 2020-03-29.Dark grey ribbon = 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (note that when the rate of growth is negative the doubling time is assumed to be infinite), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates are shown until the 2020-03-29. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-03-29 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmed cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates are shown up to the 2020-03-29. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates are shown up to the 2020-03-29. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-29. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-29. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Latest estimates (as of the 2020-03-29)

Table 2: Latest estimates (as of the 2020-03-29) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.
State New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling time (days)
Alabama 180 (135 – 231) Likely increasing 1.2 (1 – 1.4) 16 (7.8 – Inf)
Alaska 179 (127 – 224) Likely increasing 1.2 (1 – 1.5) 18 (8.2 – Inf)
Arizona 202 (143 – 256) Likely increasing 1.1 (0.9 – 1.3) 33 (11 – Inf)
Arkansas 77 (38 – 112) Likely increasing 1.2 (0.9 – 1.6) 17 (5.8 – Inf)
California 1256 (1113 – 1365) Increasing 1.1 (1 – 1.2) 55 (23 – Inf)
Colorado 314 (255 – 375) Unsure 1 (0.8 – 1.1) -66 (30 – Inf)
Connecticut 788 (694 – 894) Increasing 1.3 (1.1 – 1.4) 14 (9.7 – 27)
Delaware 118 (82 – 162) Increasing 1.5 (1.2 – 1.9) 7.6 (4.4 – 24)
District of Columbia 117 (75 – 157) Increasing 1.3 (1 – 1.6) 13 (6.1 – Inf)
Florida 1121 (1007 – 1238) Likely increasing 1.1 (1 – 1.2) 55 (22 – Inf)
Georgia 840 (737 – 946) Increasing 1.2 (1.1 – 1.4) 18 (11 – 42)
Guam 49 (6 – 89) Likely increasing 1.6 (0.7 – 2.4) 9.3 (3 – Inf)
Hawaii 356 (290 – 416) Increasing 1.3 (1.1 – 1.5) 13 (7.9 – 33)
Idaho 65 (27 – 97) Likely decreasing 0.8 (0.5 – 1.1) -11 (39 – Inf)
Illinois 1147 (1016 – 1250) Increasing 1.2 (1.1 – 1.3) 22 (14 – 51)
Indiana 515 (427 – 588) Increasing 1.2 (1.1 – 1.4) 19 (11 – 70)
Iowa 94 (52 – 125) Likely increasing 1.2 (0.9 – 1.6) 20 (6.8 – Inf)
Kansas 80 (41 – 116) Likely increasing 1.2 (0.8 – 1.5) 21 (6.4 – Inf)
Kentucky 123 (82 – 160) Increasing 1.3 (1 – 1.6) 14 (6.2 – Inf)
Louisiana 1436 (1298 – 1564) Likely increasing 1.1 (1 – 1.3) 26 (14 – 350)
Maine 35 (4 – 58) Unsure 1.1 (0.5 – 1.5) -55 (5.8 – Inf)
Maryland 408 (330 – 473) Increasing 1.2 (1 – 1.4) 17 (9.6 – 76)
Massachusetts 1217 (1096 – 1340) Increasing 1.1 (1 – 1.2) 31 (17 – 180)
Michigan 1571 (1419 – 1701) Increasing 1.1 (1 – 1.2) 31 (17 – 120)
Minnesota 74 (43 – 108) Unsure 1.2 (0.8 – 1.5) 30 (7.1 – Inf)
Mississippi 148 (101 – 189) Likely increasing 1.2 (0.9 – 1.4) 22 (8.2 – Inf)
Missouri 245 (184 – 292) Likely increasing 1.1 (0.9 – 1.3) 39 (12 – Inf)
Montana 292 (227 – 344) Increasing 1.3 (1.1 – 1.5) 14 (8.2 – 64)
Nebraska 57 (25 – 86) Likely increasing 1.4 (0.9 – 1.9) 11 (4.3 – Inf)
Nevada 135 (82 – 173) Unsure 1 (0.8 – 1.2) -470 (13 – Inf)
New Hampshire 63 (26 – 95) Unsure 1.1 (0.8 – 1.5) 50 (6.7 – Inf)
New Jersey 3627 (3407 – 3819) Increasing 1.1 (1 – 1.2) 34 (21 – 80)
New Mexico 83 (48 – 117) Increasing 1.4 (1 – 1.7) 11 (5 – Inf)
New York 9126 (8786 – 9458) Increasing 1.1 (1 – 1.1) 62 (37 – 220)
North Carolina 281 (218 – 337) Increasing 1.3 (1.1 – 1.4) 15 (8.5 – 89)
North Dakota 202 (151 – 252) Increasing 1.3 (1.1 – 1.6) 12 (6.8 – 68)
Ohio 367 (306 – 438) Likely increasing 1.1 (1 – 1.3) 39 (14 – Inf)
Oklahoma 121 (75 – 156) Unsure 1.1 (0.9 – 1.4) 35 (9 – Inf)
Oregon 80 (44 – 119) Unsure 1.1 (0.8 – 1.4) 75 (8 – Inf)
Pennsylvania 1523 (1384 – 1649) Increasing 1.3 (1.2 – 1.4) 13 (9.7 – 19)
Puerto Rico 56 (25 – 88) Unsure 1.2 (0.8 – 1.6) 37 (6.2 – Inf)
Rhode Island 134 (88 – 176) Increasing 1.3 (1 – 1.6) 13 (6.3 – Inf)
South Carolina 179 (126 – 224) Unsure 1.1 (0.8 – 1.3) 160 (14 – Inf)
South Dakota 44 (14 – 68) Likely increasing 1.5 (0.9 – 2.1) 8.4 (3.4 – Inf)
Tennessee 244 (186 – 296) Unsure 0.9 (0.8 – 1.1) -30 (42 – Inf)
Texas 829 (733 – 936) Increasing 1.2 (1.1 – 1.4) 16 (11 – 34)
Utah 132 (80 – 180) Likely increasing 1.1 (0.9 – 1.4) 35 (8.5 – Inf)
Vermont 56 (23 – 86) Likely increasing 1.3 (0.8 – 1.8) 12 (4.5 – Inf)
Virgin Islands 50 (20 – 79) Likely increasing 1.4 (0.9 – 1.9) 12 (4.3 – Inf)
Virginia 329 (256 – 386) Increasing 1.3 (1.1 – 1.5) 14 (8.3 – 49)
Washington 415 (347 – 489) Unsure 1 (0.9 – 1.1) -370 (24 – Inf)
West Virginia 49 (18 – 74) Likely increasing 1.5 (0.9 – 2) 8.8 (3.7 – Inf)
Wisconsin 168 (117 – 211) Unsure 1.1 (0.9 – 1.3) 41 (11 – Inf)
Wyoming 194 (145 – 244) Increasing 1.3 (1.1 – 1.6) 12 (6.6 – 59)

“2019 Novel Coronavirus Covid-19 (2019-nCoV) Data Repository.” 2020. Johns Hopkins CSSE. https://github.com/CSSEGISandData/COVID-19.

Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.

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